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1 files changed, 1001 insertions, 0 deletions

diff --git a/llama.cpp/tools/completion/completion.cpp b/llama.cpp/tools/completion/completion.cpp
new file mode 100644
index 0000000..9771327
--- /dev/null
+++ b/llama.cpp/tools/completion/completion.cpp
@@ -0,0 +1,1001 @@
+#include "arg.h"
+#include "common.h"
+#include "console.h"
+#include "log.h"
+#include "sampling.h"
+#include "llama.h"
+#include "chat.h"
+
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+#include <sstream>
+#include <string>
+#include <vector>
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+#include <signal.h>
+#include <unistd.h>
+#elif defined (_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#ifndef NOMINMAX
+#define NOMINMAX
+#endif
+#include <windows.h>
+#include <signal.h>
+#endif
+
+#if defined(_MSC_VER)
+#pragma warning(disable: 4244 4267) // possible loss of data
+#endif
+
+static llama_context           ** g_ctx;
+static llama_model             ** g_model;
+static common_sampler          ** g_smpl;
+static common_params            * g_params;
+static std::vector<llama_token> * g_input_tokens;
+static std::ostringstream       * g_output_ss;
+static std::vector<llama_token> * g_output_tokens;
+static bool is_interacting  = false;
+static bool need_insert_eot = false;
+
+static void print_usage(int argc, char ** argv) {
+    (void) argc;
+
+    LOG("\nexample usage:\n");
+    LOG("\n  text generation:     %s -m your_model.gguf -p \"I believe the meaning of life is\" -n 128 -no-cnv\n", argv[0]);
+    LOG("\n  chat (conversation): %s -m your_model.gguf -sys \"You are a helpful assistant\"\n", argv[0]);
+    LOG("\n");
+}
+
+static bool file_exists(const std::string & path) {
+    std::ifstream f(path.c_str());
+    return f.good();
+}
+
+static bool file_is_empty(const std::string & path) {
+    std::ifstream f;
+    f.exceptions(std::ifstream::failbit | std::ifstream::badbit);
+    f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate);
+    return f.tellg() == 0;
+}
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+static void sigint_handler(int signo) {
+    if (signo == SIGINT) {
+        if (!is_interacting && g_params->interactive) {
+            is_interacting  = true;
+            need_insert_eot = true;
+        } else {
+            console::cleanup();
+            LOG("\n");
+            common_perf_print(*g_ctx, *g_smpl);
+
+            // make sure all logs are flushed
+            LOG("Interrupted by user\n");
+            common_log_pause(common_log_main());
+
+            _exit(130);
+        }
+    }
+}
+#endif
+
+int main(int argc, char ** argv) {
+    common_params params;
+    g_params = &params;
+
+    if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMPLETION, print_usage)) {
+        return 1;
+    }
+
+    common_init();
+
+    auto & sparams = params.sampling;
+
+    // save choice to use color for later
+    // (note for later: this is a slightly awkward choice)
+    console::init(params.simple_io, params.use_color);
+    atexit([]() { console::cleanup(); });
+
+    if (params.embedding) {
+        LOG_ERR("************\n");
+        LOG_ERR("%s: please use the 'embedding' tool for embedding calculations\n", __func__);
+        LOG_ERR("************\n\n");
+
+        return 0;
+    }
+
+    if (params.n_ctx != 0 && params.n_ctx < 8) {
+        LOG_WRN("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
+        params.n_ctx = 8;
+    }
+
+    if (params.rope_freq_base != 0.0) {
+        LOG_WRN("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base);
+    }
+
+    if (params.rope_freq_scale != 0.0) {
+        LOG_WRN("%s: warning: scaling RoPE frequency by %g.\n", __func__, params.rope_freq_scale);
+    }
+
+    LOG_INF("%s: llama backend init\n", __func__);
+
+    llama_backend_init();
+    llama_numa_init(params.numa);
+
+    llama_model * model = nullptr;
+    llama_context * ctx = nullptr;
+    common_sampler * smpl = nullptr;
+
+    g_model = &model;
+    g_ctx = &ctx;
+    g_smpl = &smpl;
+
+    std::vector<common_chat_msg> chat_msgs;
+
+    // load the model and apply lora adapter, if any
+    LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__);
+
+    auto llama_init = common_init_from_params(params);
+
+    ctx   = llama_init->context();
+    model = llama_init->model();
+    smpl  = llama_init->sampler(0);
+
+    if (ctx == NULL) {
+        LOG_ERR("%s: error: unable to create context\n", __func__);
+        return 1;
+    }
+
+    llama_memory_t mem = llama_get_memory(ctx);
+    const llama_vocab * vocab = llama_model_get_vocab(model);
+
+    // note: the time for chat template initialization is not negligible:
+    auto chat_templates = common_chat_templates_init(model, params.chat_template);
+
+    // start measuring performance timings from here
+    llama_perf_context_reset(ctx);
+
+    LOG_INF("%s: llama threadpool init, n_threads = %d\n", __func__, (int) params.cpuparams.n_threads);
+
+    auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+    if (!cpu_dev) {
+        LOG_ERR("%s: no CPU backend found\n", __func__);
+        return 1;
+    }
+    auto * reg = ggml_backend_dev_backend_reg(cpu_dev);
+    auto * ggml_threadpool_new_fn = (decltype(ggml_threadpool_new) *) ggml_backend_reg_get_proc_address(reg, "ggml_threadpool_new");
+    auto * ggml_threadpool_free_fn = (decltype(ggml_threadpool_free) *) ggml_backend_reg_get_proc_address(reg, "ggml_threadpool_free");
+
+    struct ggml_threadpool_params tpp_batch =
+            ggml_threadpool_params_from_cpu_params(params.cpuparams_batch);
+    struct ggml_threadpool_params tpp =
+            ggml_threadpool_params_from_cpu_params(params.cpuparams);
+
+    if (!set_process_priority(params.cpuparams.priority)) {
+        LOG_ERR("%s: error: failed to set process priority\n", __func__);
+        return 1;
+    }
+
+    struct ggml_threadpool * threadpool_batch = NULL;
+    if (!ggml_threadpool_params_match(&tpp, &tpp_batch)) {
+        threadpool_batch = ggml_threadpool_new_fn(&tpp_batch);
+        if (!threadpool_batch) {
+            LOG_ERR("%s: batch threadpool create failed : n_threads %d\n", __func__, tpp_batch.n_threads);
+            return 1;
+        }
+
+        // start the non-batch threadpool in the paused state
+        tpp.paused = true;
+    }
+
+    struct ggml_threadpool * threadpool = ggml_threadpool_new_fn(&tpp);
+    if (!threadpool) {
+        LOG_ERR("%s: threadpool create failed : n_threads %d\n", __func__, tpp.n_threads);
+        return 1;
+    }
+
+    llama_attach_threadpool(ctx, threadpool, threadpool_batch);
+
+    const int n_ctx_train = llama_model_n_ctx_train(model);
+    const int n_ctx = llama_n_ctx(ctx);
+
+    if (n_ctx > n_ctx_train) {
+        LOG_WRN("%s: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, n_ctx);
+    }
+
+    // auto enable conversation mode if chat template is available
+    const bool has_chat_template = common_chat_templates_was_explicit(chat_templates.get());
+    if (params.conversation_mode == COMMON_CONVERSATION_MODE_AUTO) {
+        if (has_chat_template) {
+            LOG_INF("%s: chat template is available, enabling conversation mode (disable it with -no-cnv)\n", __func__);
+            params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED;
+        } else {
+            params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED;
+        }
+    }
+
+    // in case user force-activate conversation mode (via -cnv) without proper chat template, we show a warning
+    if (params.conversation_mode && !has_chat_template) {
+        LOG_WRN("%s: chat template is not available or is not supported. This may cause the model to output suboptimal responses\n", __func__);
+    }
+
+    // print chat template example in conversation mode
+    if (params.conversation_mode) {
+        if (params.enable_chat_template) {
+            if (!params.prompt.empty() && params.system_prompt.empty()) {
+                LOG_WRN("*** User-specified prompt will pre-start conversation, did you mean to set --system-prompt (-sys) instead?\n");
+            }
+
+            LOG_INF("%s: chat template example:\n%s\n", __func__, common_chat_format_example(chat_templates.get(), params.use_jinja, params.default_template_kwargs).c_str());
+        } else {
+            LOG_INF("%s: in-suffix/prefix is specified, chat template will be disabled\n", __func__);
+        }
+    }
+
+    // print system information
+    {
+        LOG_INF("\n");
+        LOG_INF("%s\n", common_params_get_system_info(params).c_str());
+        LOG_INF("\n");
+    }
+
+    std::string path_session = params.path_prompt_cache;
+    std::vector<llama_token> session_tokens;
+
+    if (!path_session.empty()) {
+        LOG_INF("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
+        if (!file_exists(path_session)) {
+            LOG_INF("%s: session file does not exist, will create.\n", __func__);
+        } else if (file_is_empty(path_session)) {
+            LOG_INF("%s: The session file is empty. A new session will be initialized.\n", __func__);
+        } else {
+            // The file exists and is not empty
+            session_tokens.resize(n_ctx);
+            size_t n_token_count_out = 0;
+            if (!llama_state_load_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
+                LOG_ERR("%s: failed to load session file '%s'\n", __func__, path_session.c_str());
+                return 1;
+            }
+            session_tokens.resize(n_token_count_out);
+            LOG_INF("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size());
+        }
+    }
+
+    const bool add_bos = llama_vocab_get_add_bos(vocab) && !params.use_jinja;
+    if (!llama_model_has_encoder(model)) {
+        GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
+    }
+
+    LOG_DBG("n_ctx: %d, add_bos: %d\n", n_ctx, add_bos);
+
+    std::vector<llama_token> embd_inp;
+
+    bool waiting_for_first_input = false;
+    auto chat_add_and_format = [&chat_msgs, &chat_templates](const std::string & role, const std::string & content) {
+        common_chat_msg new_msg;
+        new_msg.role = role;
+        new_msg.content = content;
+        auto formatted = common_chat_format_single(chat_templates.get(), chat_msgs, new_msg, role == "user", g_params->use_jinja);
+        chat_msgs.push_back(new_msg);
+        LOG_DBG("formatted: '%s'\n", formatted.c_str());
+        return formatted;
+    };
+
+    std::string prompt;
+    {
+        if (params.conversation_mode && params.enable_chat_template) {
+            if (!params.system_prompt.empty()) {
+                // format the system prompt (will use template default if empty)
+                chat_add_and_format("system", params.system_prompt);
+            }
+
+            if (!params.prompt.empty()) {
+                // format and append the user prompt
+                chat_add_and_format("user", params.prompt);
+            } else {
+                waiting_for_first_input = true;
+            }
+
+            if (!params.system_prompt.empty() || !params.prompt.empty()) {
+                common_chat_templates_inputs inputs;
+                inputs.use_jinja = g_params->use_jinja;
+                inputs.messages = chat_msgs;
+                inputs.add_generation_prompt = !params.prompt.empty();
+
+                prompt = common_chat_templates_apply(chat_templates.get(), inputs).prompt;
+            }
+        } else {
+            // otherwise use the prompt as is
+            prompt = params.prompt;
+        }
+
+        if (params.interactive_first || !prompt.empty() || session_tokens.empty()) {
+            LOG_DBG("tokenize the prompt\n");
+            embd_inp = common_tokenize(ctx, prompt, true, true);
+        } else {
+            LOG_DBG("use session tokens\n");
+            embd_inp = session_tokens;
+        }
+
+        LOG_DBG("prompt: \"%s\"\n", prompt.c_str());
+        LOG_DBG("tokens: %s\n", string_from(ctx, embd_inp).c_str());
+    }
+
+    // Should not run without any tokens
+    if (!waiting_for_first_input && embd_inp.empty()) {
+        if (add_bos) {
+            embd_inp.push_back(llama_vocab_bos(vocab));
+            LOG_WRN("embd_inp was considered empty and bos was added: %s\n", string_from(ctx, embd_inp).c_str());
+        } else {
+            LOG_ERR("input is empty\n");
+            return -1;
+        }
+    }
+
+    // Tokenize negative prompt
+    if ((int) embd_inp.size() > n_ctx - 4) {
+        LOG_ERR("%s: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
+        return 1;
+    }
+
+    bool session_do_save = false;
+
+    {
+        size_t n_match = 0;
+
+        if (!session_tokens.empty()) {
+            for (llama_token id : session_tokens) {
+                if (n_match >= embd_inp.size() || id != embd_inp[n_match]) {
+                    break;
+                }
+                n_match++;
+            }
+            if (params.prompt.empty() && n_match == embd_inp.size()) {
+                LOG_INF("%s: using full prompt from session file\n", __func__);
+            } else if (n_match >= embd_inp.size()) {
+                LOG_INF("%s: session file has exact match for prompt!\n", __func__);
+            } else if (n_match < (embd_inp.size() / 2)) {
+                LOG_WRN("%s: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
+                        __func__, n_match, embd_inp.size());
+            } else {
+                LOG_INF("%s: session file matches %zu / %zu tokens of prompt\n",
+                        __func__, n_match, embd_inp.size());
+            }
+
+            if (session_tokens.size() == n_match) {
+                // [TAG_CONTEXT_STATE_LOGITS]
+                // in this case, we are going to reuse the logits from the session
+                // if we ever decide to remove the logits from the session, we need to handle this somehow
+                // ref: https://github.com/ggml-org/llama.cpp/pull/18862#issuecomment-3756330941
+            }
+
+            // remove any "future" tokens that we might have inherited from the previous session
+            if (session_tokens.size() > n_match) {
+                if (!llama_memory_seq_rm(mem, -1, n_match, -1)) {
+                    LOG_WRN("%s: unable to resuse common prefix (for example, when the memory is recurrent)\n", __func__);
+                    llama_memory_clear(mem, true);
+                    session_tokens.clear();
+                    n_match = 0;
+                } else {
+                    session_tokens.resize(n_match);
+                }
+            }
+        }
+
+        session_do_save = !path_session.empty() && n_match < embd_inp.size() && !params.prompt_cache_ro;
+    }
+
+    // number of tokens to keep when resetting context
+    if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) {
+        params.n_keep = (int)embd_inp.size();
+    } else {
+        params.n_keep += add_bos; // always keep the BOS token
+    }
+
+    if (params.conversation_mode) {
+        if (params.single_turn && !params.prompt.empty()) {
+            params.interactive = false;
+            params.interactive_first = false;
+        } else {
+            params.interactive_first = true;
+        }
+    }
+
+    // enable interactive mode if interactive start is specified
+    if (params.interactive_first) {
+        params.interactive = true;
+    }
+
+    if (params.verbose_prompt) {
+        LOG_INF("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
+        LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+        for (int i = 0; i < (int) embd_inp.size(); i++) {
+            LOG_INF("%6d -> '%s'\n", embd_inp[i], common_token_to_piece(ctx, embd_inp[i]).c_str());
+        }
+
+        if (params.n_keep > add_bos) {
+            LOG_INF("%s: static prompt based on n_keep: '", __func__);
+            for (int i = 0; i < params.n_keep; i++) {
+                LOG_CNT("%s", common_token_to_piece(ctx, embd_inp[i]).c_str());
+            }
+            LOG_CNT("'\n");
+        }
+        LOG_INF("\n");
+    }
+
+    // ctrl+C handling
+    {
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+        struct sigaction sigint_action;
+        sigint_action.sa_handler = sigint_handler;
+        sigemptyset (&sigint_action.sa_mask);
+        sigint_action.sa_flags = 0;
+        sigaction(SIGINT, &sigint_action, NULL);
+#elif defined (_WIN32)
+        auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
+            return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true) : false;
+        };
+        SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
+#endif
+    }
+
+    if (params.interactive) {
+        LOG_INF("%s: interactive mode on.\n", __func__);
+
+        if (!params.antiprompt.empty()) {
+            for (const auto & antiprompt : params.antiprompt) {
+                LOG_INF("Reverse prompt: '%s'\n", antiprompt.c_str());
+                if (params.verbose_prompt) {
+                    auto tmp = common_tokenize(ctx, antiprompt, false, true);
+                    for (int i = 0; i < (int) tmp.size(); i++) {
+                        LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
+                    }
+                }
+            }
+        }
+
+        if (params.input_prefix_bos) {
+            LOG_INF("Input prefix with BOS\n");
+        }
+
+        if (!params.input_prefix.empty()) {
+            LOG_INF("Input prefix: '%s'\n", params.input_prefix.c_str());
+            if (params.verbose_prompt) {
+                auto tmp = common_tokenize(ctx, params.input_prefix, true, true);
+                for (int i = 0; i < (int) tmp.size(); i++) {
+                    LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
+                }
+            }
+        }
+
+        if (!params.input_suffix.empty()) {
+            LOG_INF("Input suffix: '%s'\n", params.input_suffix.c_str());
+            if (params.verbose_prompt) {
+                auto tmp = common_tokenize(ctx, params.input_suffix, false, true);
+                for (int i = 0; i < (int) tmp.size(); i++) {
+                    LOG_INF("%6d -> '%s'\n", tmp[i], common_token_to_piece(ctx, tmp[i]).c_str());
+                }
+            }
+        }
+    }
+
+    LOG_INF("sampler seed: %u\n",     common_sampler_get_seed(smpl));
+    LOG_INF("sampler params: \n%s\n", sparams.print().c_str());
+    LOG_INF("sampler chain: %s\n",    common_sampler_print(smpl).c_str());
+
+    LOG_INF("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
+
+    // group-attention state
+    // number of grouped KV tokens so far (used only if params.grp_attn_n > 1)
+    int ga_i = 0;
+
+    const int ga_n = params.grp_attn_n;
+    const int ga_w = params.grp_attn_w;
+
+    if (ga_n != 1) {
+        GGML_ASSERT(ga_n > 0                    && "grp_attn_n must be positive");                     // NOLINT
+        GGML_ASSERT(ga_w % ga_n == 0            && "grp_attn_w must be a multiple of grp_attn_n");     // NOLINT
+      //GGML_ASSERT(n_ctx_train % ga_w == 0     && "n_ctx_train must be a multiple of grp_attn_w");    // NOLINT
+      //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * grp_attn_n"); // NOLINT
+        LOG_INF("self-extend: n_ctx_train = %d, grp_attn_n = %d, grp_attn_w = %d\n", n_ctx_train, ga_n, ga_w);
+    }
+    LOG_INF("\n");
+
+    if (params.interactive) {
+        const char * control_message;
+        if (params.multiline_input) {
+            control_message = " - To return control to the AI, end your input with '\\'.\n"
+                              " - To return control without starting a new line, end your input with '/'.\n";
+        } else {
+            control_message = " - Press Return to return control to the AI.\n"
+                              " - To return control without starting a new line, end your input with '/'.\n"
+                              " - If you want to submit another line, end your input with '\\'.\n";
+        }
+        LOG_INF("== Running in interactive mode. ==\n");
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
+        LOG_INF(       " - Press Ctrl+C to interject at any time.\n");
+#endif
+        LOG_INF(       "%s", control_message);
+        if (params.conversation_mode && params.enable_chat_template && params.system_prompt.empty()) {
+            LOG_INF(   " - Not using system message. To change it, set a different value via -sys PROMPT\n");
+        }
+        LOG_INF("\n");
+
+        is_interacting = params.interactive_first;
+    }
+
+    bool is_antiprompt = false;
+    bool input_echo    = true;
+    bool display       = true;
+
+    int n_past             = 0;
+    int n_remain           = params.n_predict;
+    int n_consumed         = 0;
+    int n_session_consumed = 0;
+
+    std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
+    std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
+    std::ostringstream output_ss;     g_output_ss     = &output_ss;
+    std::ostringstream assistant_ss; // for storing current assistant message, used in conversation mode
+
+    // the first thing we will do is to output the prompt, so set color accordingly
+    console::set_display(DISPLAY_TYPE_PROMPT);
+    display = params.display_prompt;
+
+    std::vector<llama_token> embd;
+
+    // single-token antiprompts
+    std::vector<llama_token> antiprompt_token;
+
+    for (const std::string & antiprompt : params.antiprompt) {
+        auto ids = ::common_tokenize(ctx, antiprompt, false, true);
+        if (ids.size() == 1) {
+            antiprompt_token.push_back(ids[0]);
+        }
+    }
+
+    if (llama_model_has_encoder(model)) {
+        int enc_input_size = embd_inp.size();
+        llama_token * enc_input_buf = embd_inp.data();
+
+        if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size))) {
+            LOG_ERR("%s : failed to eval\n", __func__);
+            return 1;
+        }
+
+        llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
+        if (decoder_start_token_id == LLAMA_TOKEN_NULL) {
+            decoder_start_token_id = llama_vocab_bos(vocab);
+        }
+
+        embd_inp.clear();
+        embd_inp.push_back(decoder_start_token_id);
+    }
+
+    while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
+        // predict
+        if (!embd.empty()) {
+            // Note: (n_ctx - 4) here is to match the logic for commandline prompt handling via
+            // --prompt or --file which uses the same value.
+            int max_embd_size = n_ctx - 4;
+
+            // Ensure the input doesn't exceed the context size by truncating embd if necessary.
+            if ((int) embd.size() > max_embd_size) {
+                const int skipped_tokens = (int) embd.size() - max_embd_size;
+                embd.resize(max_embd_size);
+
+                console::set_display(DISPLAY_TYPE_ERROR);
+                LOG_WRN("<<input too long: skipped %d token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
+                console::set_display(DISPLAY_TYPE_RESET);
+            }
+
+            if (ga_n == 1) {
+                // infinite text generation via context shifting
+                // if we run out of context:
+                // - take the n_keep first tokens from the original prompt (via n_past)
+                // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
+
+                if (n_past + (int) embd.size() >= n_ctx) {
+                    if (!params.ctx_shift){
+                        LOG_WRN("\n\n%s: context full and context shift is disabled => stopping\n", __func__);
+                        break;
+                    }
+
+                    if (params.n_predict == -2) {
+                        LOG_WRN("\n\n%s: context full and n_predict == %d => stopping\n", __func__, params.n_predict);
+                        break;
+                    }
+
+                    const int n_left    = n_past - params.n_keep;
+                    const int n_discard = n_left/2;
+
+                    LOG_DBG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d, n_discard = %d\n",
+                            n_past, n_left, n_ctx, params.n_keep, n_discard);
+
+                    llama_memory_seq_rm (mem, 0, params.n_keep            , params.n_keep + n_discard);
+                    llama_memory_seq_add(mem, 0, params.n_keep + n_discard, n_past, -n_discard);
+
+                    n_past -= n_discard;
+
+                    LOG_DBG("after swap: n_past = %d\n", n_past);
+
+                    LOG_DBG("embd: %s\n", string_from(ctx, embd).c_str());
+
+                    LOG_DBG("clear session path\n");
+                    path_session.clear();
+                }
+            } else {
+                // context extension via Self-Extend
+                while (n_past >= ga_i + ga_w) {
+                    const int ib = (ga_n*ga_i)/ga_w;
+                    const int bd = (ga_w/ga_n)*(ga_n - 1);
+                    const int dd = (ga_w/ga_n) - ib*bd - ga_w;
+
+                    LOG_DBG("\n");
+                    LOG_DBG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i, n_past, ib*bd, ga_i + ib*bd, n_past + ib*bd);
+                    LOG_DBG("div:   [%6d, %6d] / %6d -> [%6d, %6d]\n", ga_i + ib*bd, ga_i + ib*bd + ga_w, ga_n, (ga_i + ib*bd)/ga_n, (ga_i + ib*bd + ga_w)/ga_n);
+                    LOG_DBG("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", ga_i + ib*bd + ga_w, n_past + ib*bd, dd, ga_i + ib*bd + ga_w + dd, n_past + ib*bd + dd);
+
+                    llama_memory_seq_add(mem, 0, ga_i,                n_past,              ib*bd);
+                    llama_memory_seq_div(mem, 0, ga_i + ib*bd,        ga_i + ib*bd + ga_w, ga_n);
+                    llama_memory_seq_add(mem, 0, ga_i + ib*bd + ga_w, n_past + ib*bd,      dd);
+
+                    n_past -= bd;
+
+                    ga_i += ga_w/ga_n;
+
+                    LOG_DBG("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", n_past + bd, n_past, ga_i);
+                }
+            }
+
+            // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
+            if (n_session_consumed < (int) session_tokens.size()) {
+                size_t i = 0;
+                for ( ; i < embd.size(); i++) {
+                    if (embd[i] != session_tokens[n_session_consumed]) {
+                        session_tokens.resize(n_session_consumed);
+                        break;
+                    }
+
+                    n_past++;
+                    n_session_consumed++;
+
+                    if (n_session_consumed >= (int) session_tokens.size()) {
+                        ++i;
+                        break;
+                    }
+                }
+                if (i > 0) {
+                    embd.erase(embd.begin(), embd.begin() + i);
+                }
+            }
+
+            if (!embd.empty()) {
+                int n_eval = (int) embd.size();
+                LOG_DBG("eval: %s\n", string_from(ctx, embd).c_str());
+
+                GGML_ASSERT(n_eval <= params.n_batch);
+                if (llama_decode(ctx, llama_batch_get_one(embd.data(), n_eval))) {
+                    LOG_ERR("%s : failed to eval\n", __func__);
+                    return 1;
+                }
+
+                n_past += n_eval;
+
+                LOG_DBG("n_past = %d\n", n_past);
+                // Display total tokens alongside total time
+                if (params.n_print > 0 && n_past % params.n_print == 0) {
+                    LOG_DBG("\n\033[31mTokens consumed so far = %d / %d \033[0m\n", n_past, n_ctx);
+                }
+            }
+
+            if (!embd.empty() && !path_session.empty()) {
+                session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
+                n_session_consumed = session_tokens.size();
+            }
+        }
+
+        embd.clear();
+
+        if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
+            // optionally save the session on first sample (for faster prompt loading next time)
+            if (session_do_save) {
+                session_do_save = false;
+                llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
+
+                LOG_DBG("saved session to %s\n", path_session.c_str());
+            }
+
+            const llama_token id = common_sampler_sample(smpl, ctx, -1);
+
+            common_sampler_accept(smpl, id, /* accept_grammar= */ true);
+
+            // LOG_DBG("last: %s\n", string_from(ctx, smpl->prev.to_vector()).c_str());
+
+            embd.push_back(id);
+
+            if (params.conversation_mode && !waiting_for_first_input && !llama_vocab_is_eog(vocab, id)) {
+                assistant_ss << common_token_to_piece(ctx, id, false);
+            }
+
+            // echo this to console
+            input_echo = true;
+
+            // decrement remaining sampling budget
+            --n_remain;
+
+            LOG_DBG("n_remain: %d\n", n_remain);
+        } else {
+            // some user input remains from prompt or interaction, forward it to processing
+            LOG_DBG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
+            while ((int) embd_inp.size() > n_consumed) {
+                embd.push_back(embd_inp[n_consumed]);
+
+                // push the prompt in the sampling context in order to apply repetition penalties later
+                // for the prompt, we don't apply grammar rules
+                common_sampler_accept(smpl, embd_inp[n_consumed], /* accept_grammar= */ false);
+
+                ++n_consumed;
+                if ((int) embd.size() == params.n_batch) {
+                    break;
+                }
+            }
+        }
+
+        // display text
+        if (input_echo && display) {
+            for (auto id : embd) {
+                const std::string token_str = common_token_to_piece(ctx, id, params.special);
+
+                // Console/Stream Output
+                LOG("%s", token_str.c_str());
+
+                // Record Displayed Tokens To Log
+                // Note: Generated tokens are created one by one hence this check
+                if (embd.size() > 1) {
+                    // Incoming Requested Tokens
+                    input_tokens.push_back(id);
+                } else {
+                    // Outgoing Generated Tokens
+                    output_tokens.push_back(id);
+                    output_ss << token_str;
+                }
+            }
+        }
+
+        // reset color to default if there is no pending user input
+        if (input_echo && (int) embd_inp.size() == n_consumed) {
+            console::set_display(DISPLAY_TYPE_RESET);
+            display = true;
+        }
+
+        // if not currently processing queued inputs;
+        if ((int) embd_inp.size() <= n_consumed) {
+            // check for reverse prompt in the last n_prev tokens
+            if (!params.antiprompt.empty()) {
+                const int n_prev = 32;
+                const std::string last_output = common_sampler_prev_str(smpl, ctx, n_prev);
+
+                is_antiprompt = false;
+                // Check if each of the reverse prompts appears at the end of the output.
+                // If we're not running interactively, the reverse prompt might be tokenized with some following characters
+                // so we'll compensate for that by widening the search window a bit.
+                for (std::string & antiprompt : params.antiprompt) {
+                    size_t extra_padding = params.interactive ? 0 : 2;
+                    size_t search_start_pos = last_output.length() > static_cast<size_t>(antiprompt.length() + extra_padding)
+                        ? last_output.length() - static_cast<size_t>(antiprompt.length() + extra_padding)
+                        : 0;
+
+                    if (last_output.find(antiprompt, search_start_pos) != std::string::npos) {
+                        if (params.interactive) {
+                            is_interacting = true;
+                        }
+                        is_antiprompt = true;
+                        break;
+                    }
+                }
+
+                // check for reverse prompt using special tokens
+                // avoid calling common_sampler_last() if last_output is empty
+                if (!last_output.empty()) {
+                    llama_token last_token = common_sampler_last(smpl);
+                    for (auto token : antiprompt_token) {
+                        if (token == last_token) {
+                            if (params.interactive) {
+                                is_interacting = true;
+                            }
+                            is_antiprompt = true;
+                            break;
+                        }
+                    }
+                }
+
+                if (is_antiprompt) {
+                    LOG_DBG("found antiprompt: %s\n", last_output.c_str());
+                }
+            }
+
+            // deal with end of generation tokens in interactive mode
+            if (!waiting_for_first_input && llama_vocab_is_eog(vocab, common_sampler_last(smpl))) {
+                LOG_DBG("found an EOG token\n");
+
+                if (params.interactive) {
+                    if (!params.antiprompt.empty()) {
+                        // tokenize and inject first reverse prompt
+                        const auto first_antiprompt = common_tokenize(ctx, params.antiprompt.front(), false, true);
+                        embd_inp.insert(embd_inp.end(), first_antiprompt.begin(), first_antiprompt.end());
+                        is_antiprompt = true;
+                    }
+
+                    if (params.enable_chat_template) {
+                        chat_add_and_format("assistant", assistant_ss.str());
+                    }
+                    is_interacting = true;
+                    LOG("\n");
+                }
+            }
+
+            if (params.conversation_mode && !waiting_for_first_input) {
+                if (!prompt.empty()) {
+                    prompt.clear();
+                    is_interacting = false;
+                }
+            }
+
+            if ((n_past > 0 || waiting_for_first_input) && is_interacting) {
+                LOG_DBG("waiting for user input\n");
+
+                if (params.conversation_mode) {
+                    LOG("\n> ");
+                }
+
+                if (params.input_prefix_bos) {
+                    LOG_DBG("adding input prefix BOS token\n");
+                    embd_inp.push_back(llama_vocab_bos(vocab));
+                }
+
+                std::string buffer;
+                if (!params.input_prefix.empty() && !params.conversation_mode) {
+                    LOG_DBG("appending input prefix: '%s'\n", params.input_prefix.c_str());
+                    LOG("%s", params.input_prefix.c_str());
+                }
+
+                // color user input only
+                console::set_display(DISPLAY_TYPE_USER_INPUT);
+                display = params.display_prompt;
+
+                std::string line;
+                bool another_line = true;
+                do {
+                    another_line = console::readline(line, params.multiline_input);
+                    buffer += line;
+                } while (another_line);
+
+                // done taking input, reset color
+                console::set_display(DISPLAY_TYPE_RESET);
+                display = true;
+
+                if (buffer.empty()) { // Ctrl+D on empty line exits
+                    LOG("EOF by user\n");
+                    break;
+                }
+
+                if (buffer.back() == '\n') {
+                    // Implement #587:
+                    // If the user wants the text to end in a newline,
+                    // this should be accomplished by explicitly adding a newline by using \ followed by return,
+                    // then returning control by pressing return again.
+                    buffer.pop_back();
+                }
+
+                if (buffer.empty()) { // Enter key on empty line lets the user pass control back
+                    LOG_DBG("empty line, passing control back\n");
+                } else { // Add tokens to embd only if the input buffer is non-empty
+                    // append input suffix if any
+                    if (!params.input_suffix.empty() && !params.conversation_mode) {
+                        LOG_DBG("appending input suffix: '%s'\n", params.input_suffix.c_str());
+                        LOG("%s", params.input_suffix.c_str());
+                    }
+
+                    LOG_DBG("buffer: '%s'\n", buffer.c_str());
+
+                    const size_t original_size = embd_inp.size();
+
+                    if (params.escape) {
+                        string_process_escapes(buffer);
+                    }
+
+                    bool format_chat = params.conversation_mode && params.enable_chat_template;
+                    std::string user_inp = format_chat
+                        ? chat_add_and_format("user", std::move(buffer))
+                        : std::move(buffer);
+                    // TODO: one inconvenient of current chat template implementation is that we can't distinguish between user input and special tokens (prefix/postfix)
+                    const auto line_pfx = common_tokenize(ctx, params.input_prefix, false, true);
+                    const auto line_inp = common_tokenize(ctx, user_inp,            false, format_chat);
+                    const auto line_sfx = common_tokenize(ctx, params.input_suffix, false, true);
+
+                    LOG_DBG("input tokens: %s\n", string_from(ctx, line_inp).c_str());
+
+                    // if user stop generation mid-way, we must add EOT to finish model's last response
+                    if (need_insert_eot && format_chat) {
+                        llama_token eot = llama_vocab_eot(vocab);
+                        embd_inp.push_back(eot == LLAMA_TOKEN_NULL ? llama_vocab_eos(vocab) : eot);
+                        need_insert_eot = false;
+                    }
+
+                    embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());
+                    embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
+                    embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end());
+
+                    if (params.verbose_prompt) {
+                        LOG_INF("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size() - original_size);
+                    }
+
+                    for (size_t i = original_size; i < embd_inp.size(); ++i) {
+                        const llama_token token = embd_inp[i];
+                        const std::string token_str = common_token_to_piece(ctx, token);
+                        output_tokens.push_back(token);
+                        output_ss << token_str;
+
+                        if (params.verbose_prompt) {
+                            LOG_INF("%6d -> '%s'\n", token, token_str.c_str());
+                        }
+                    }
+
+                    // reset assistant message
+                    assistant_ss.str("");
+
+                    n_remain -= line_inp.size();
+                    LOG_DBG("n_remain: %d\n", n_remain);
+                }
+
+                input_echo = false; // do not echo this again
+            }
+
+            if (n_past > 0 || waiting_for_first_input) {
+                if (is_interacting) {
+                    common_sampler_reset(smpl);
+                }
+                is_interacting = false;
+
+                if (waiting_for_first_input && params.single_turn) {
+                    params.interactive = false;
+                    params.interactive_first = false;
+                }
+                waiting_for_first_input = false;
+            }
+        }
+
+        // end of generation
+        if (!embd.empty() && llama_vocab_is_eog(vocab, embd.back()) && !(params.interactive)) {
+            LOG(" [end of text]\n");
+            break;
+        }
+
+        // In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
+        // We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size).
+        if (params.interactive && n_remain <= 0 && params.n_predict >= 0) {
+            n_remain = params.n_predict;
+            is_interacting = true;
+        }
+    }
+
+    if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
+        LOG("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
+        llama_state_save_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
+    }
+
+    LOG("\n\n");
+    common_perf_print(ctx, smpl);
+
+    llama_backend_free();
+
+    ggml_threadpool_free_fn(threadpool);
+    ggml_threadpool_free_fn(threadpool_batch);
+
+    return 0;
+}